The Effect of Information Technology on Healthcare Improvement from Clinicians’ Perspective
Bibliographic record
Abstract
OBJECTIVE: This study investigated the perspective of clinicians about the effect of information technology (IT) on healthcare improvement. METHODS: This cross-sectional study conducted in 2014-15, developed a questionnaire to evaluate of the perspective of 281 employees at two general hospitals affiliated with Zahedan University of Medical Sciences to measure the effect of IT on improving the healthcare system. The data was analyzed using the descriptive Kolmogorov-Smirnov and Kruskal-Wallis tests. One-way ANOVA was used to compare groups. RESULTS: The overall attitude of the research population about the effect of IT on healthcare was positive, with an average score of 3.29 ± 0.90. The most influential effects of IT on the healthcare were accelerated diagnosis and treatment. The use of standardized messaging was the most effective approach for improving the healthcare system. Developing a standard mechanism for protection of data and establishing clear rules for acceptance of computer documentation by the judicial authorities were the most influential cases to increase IT effects in the healthcare system. CONCLUSION: Physicians play important roles in the successful implementation of IT because they are directly involved in the treatment of patients. Their opinions should be considered when providing or creating any type of system. The importance of budgeting for IT should be considered, because applying these systems can be capital intensive. Because application of such systems requires acceptance by legal circles of the information obtained, it is necessary for preparations to be made.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".